Three Guiding Principles for the Development of OSINT Agentic Systems

More data, more problems

The open-source intelligence (OSINT) landscape is wider, deeper, and richer than ever, but that ever-growing ocean of information has revealed a critical problem in our existing means of sifting through the various signals for rapid, actionable insights. Existing tools allow analysts to cast their nets wide, but the process of identifying salient information, validating, and synthesizing results remains labor-intensive. Analysts often spend more time managing systems and sifting through noise than following leads and drawing conclusions.

Retrieval-augmented generation (RAG) systems have gained popularity for surfacing relevant information quickly, but they operate orthogonally to existing workflows. Analysts still need to build searches, interpret results, and connect dots across a wide range of OSINT data sources. Existing systems allow them to achieve this, but the process can take considerable time, delaying actionable insights in environments where time-to-insight/decision is critical.

Designing robust agentic systems

AI agents have recently taken the spotlight as an answer to this problem across a wide range of applications. By hooking directly into the same tools analysts use, AI agents can abstract away much of the manual work, freeing up time and resources to achieve conclusive findings. In the coming years, their adoption will grow rapidly, but the value they deliver will be determined by not only how well they are implemented, but how their involvement is designed.

There exists a barrier to entry for AI solutions in many analyst workflows, namely, how can analysts trust them? Companies can evaluate metrics for general accuracy, but benchmarks only go so far in facilitating people’s willingness to use these new tools.

AI agents are not like the other tools analysts have in their arsenal. They’re novel in the sense that a robust agentic framework can interoperate with preexisting tools/infrastructure on behalf of the user semi-autonomously. A robust agentic system is not just an API that you have to tell what to do; it’s an assistant of sorts that can “reason” about a problem/question, devise the right strategy and tools for the job, and execute on a plan.

Some of these “tools” can include a fleet of other agents that can be orchestrated — each with a specific domain of expertise — to achieve a larger goal. As such, the extent of their abilities is only limited to the tools and applications at their disposal. However, if their usage is not guided (and in some cases, constrained) by a set of principles, they risk being misaligned with analysts, opaque and biased in their methods or reasoning, and can leave real analyst input out of the equation. Below, we outline three foundational principles that should guide the development of agentic systems for open-source intelligence.

Understand intent

Any AI solution that goes beyond a general use case is limited to the quality of the context and framing of the problem it has been tasked with solving. This alignment of AI behavior is often achieved with model training/tuning or prompt engineering. This poses a problem for the dynamic nature of OSINT workflows — while the traditional models of the intel lifecycle (plan, collect, process, analyze, disseminate) offer a general framework, analysts often follow intuition, adjust scope, and draw on years of tactical knowledge and tradecraft, which can be confidential. Agentic solutions operating in this space need to be persistent collaborators, capable of capturing and iterating on “what matters now.” It’s crucial, however, to strike a balance between deriving information about the investigation’s needs and not compromising the confidentiality or nuance of the analyst’s process.

From a technical standpoint, there’s the challenge of building agents that are consistently grounded in current questions and key pieces of information without being solely confined to them. One of the unique capabilities of LLM-powered agents is their ability to rapidly reason across a broad set of data as they surface overlooked patterns, propose hypotheses, and reframe understanding in light of new developments. The goal is not to replace the analyst’s own intuition but amplify and augment it — assisting with their operational needs as well as suggesting additional avenues they may otherwise not explore.

Transparency is key

Having confidence that a system is properly aligned with the analyst is not enough to put blind faith in its actions or reasoning. Provenance is essential for OSINT. Without transparency, the system becomes a black box, and in a space where human accountability is non-negotiable, such systems risk being unusable. This means analysts need end-to-end traceability through the entire agentic process — from sourcing authoritative information, what tools were used, to the steps performed to process and analyze it. This means any action initiated by agents within a system must be thoroughly logged and presented to users, and each finding or insight formed needs to be directly mapped back to its source(s).

Human-in-the-loop

Ultimately, it will be humans drawing conclusions and steering the investigation. As such, not only does the system need to understand their intentions and provide justification for its findings, it also must make the analyst an active participant, especially at key decision points. No matter how sophisticated the AI implementation is, there will be potential for mistakes.

Previewing a formulated plan of action for the user will allow them to correct these mistakes and guide the system before kicking off a complex, potentially lengthy process that isn’t what the analyst actually wants. Additionally, analysts must retain the ability to operate their existing tools manually so they can choose when and how they use AI in a given workflow. Agentic systems should integrate around human workflows, otherwise the workflow risks becoming disjointed or too restrictive. In high-stakes OSINT environments, analysts must remain firmly in the driver's seat.

Putting principles into practice

AI agents have the potential to transform workflows and act as a force multiplier for OSINT, taking analysts to results in a fraction of the time. However, their adoption is contingent on having a mindful, user-driven approach to how they are integrated into analyst workflows. Agents must be aligned not just with the data or the tools of a system, but also with the analysts themselves — their intent, their judgement, and their accountability.

As we move further into an era of increasingly autonomous tooling, we should be asking not only how much agents can do, but how much they can assist analysts in their mission. Agentic systems built with those values in mind will not just scale OSINT operations, but improve precision, reduce cognitive overhead, and bring analysts closer to the insights that matter most.